Assisting users with personalized and contextual communication content

ABSTRACT

In one embodiment, a method includes receiving, from a client system associated with a first user, a first user input by the first user, wherein the first user input is associated with a current dialog session, identifying a first language register associated with the first user based on the first user input, accessing a plurality of language-register models associated with a plurality of language registers stored in a data store, selecting a first language-register model from the plurality of language-register models based on the identified first language register, and generating a first communication content responsive to the first user input, the first communication content being personalized for the first user based on the selected first language-register model.

PRIORITY

This application claims the benefit, under 35 U.S.C. § 119(e), of U.S.Provisional Patent Application No. 62/660,876, filed 20 Apr. 2018, whichis incorporated herein by reference.

TECHNICAL FIELD

This disclosure generally relates to databases and file managementwithin network environments, and in particular relates to hardware andsoftware for smart assistant systems.

BACKGROUND

An assistant system can provide information or services on behalf of auser based on a combination of user input, location awareness, and theability to access information from a variety of online sources (such asweather conditions, traffic congestion, news, stock prices, userschedules, retail prices, etc.). The user input may include text (e.g.,online chat), especially in an instant messaging application or otherapplications, voice, images, or a combination of them. The assistantsystem may perform concierge-type services (e.g., making dinnerreservations, purchasing event tickets, making travel arrangements) orprovide information based on the user input. The assistant system mayalso perform management or data-handling tasks based on onlineinformation and events without user initiation or interaction. Examplesof those tasks that may be performed by an assistant system may includeschedule management (e.g., sending an alert to a dinner date that a useris running late due to traffic conditions, update schedules for bothparties, and change the restaurant reservation time). The assistantsystem may be enabled by the combination of computing devices,application programming interfaces (APIs), and the proliferation ofapplications on user devices.

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. profile/newsfeed posts, photo-sharing, event organization, messaging, games, oradvertisements) to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the assistant system may assist a user toobtain information or services. The assistant system may enable the userto interact with it with multi-modal user input (such as voice, text,image, video) in stateful and multi-turn conversations to getassistance. The assistant system may create and store a user profilecomprising both personal and contextual information associated with theuser. In particular embodiments, the assistant system may analyze theuser input using natural-language understanding. The analysis may bebased on the user profile for more personalized and context-awareunderstanding. The assistant system may resolve entities associated withthe user input based on the analysis. In particular embodiments, theassistant system may interact with different agents to obtaininformation or services that are associated with the resolved entities.The assistant system may generate a response for the user regarding theinformation or services by using natural-language generation. Throughthe interaction with the user, the assistant system may use dialogmanagement techniques to manage and forward the conversation flow withthe user. In particular embodiments, the assistant system may furtherassist the user to effectively and efficiently digest the obtainedinformation by summarizing the information. The assistant system mayalso assist the user to be more engaging with an online social networkby providing tools that help the user interact with the online socialnetwork (e.g., creating posts, comments, messages). The assistant systemmay additionally assist the user to manage different tasks such askeeping track of events. In particular embodiments, the assistant systemmay proactively execute tasks that are relevant to user interests andpreferences based on the user profile without a user input. Inparticular embodiments, the assistant system may check privacy settingsto ensure that accessing a user's profile or other user information andexecuting different tasks are permitted subject to the user's privacysettings.

In particular embodiments, the assistant system may automaticallyidentify a user's intent and language register based on a user input,generate a communication content suitable for such intent and languageregister for the user, detect a change of the user's intent and languageregister, and dynamically adjust the generation of the communicationcontent to fit the changed intent and language register. In linguistics,a language register characterizes a particular intent coupled with aparticular scenario, which is a linguistic term of art for a variationof a language used for a particular purpose or in a particular socialsetting. For example, a language register may reflect word choices,voice tunes (in speech), speed, etc. In particular embodiments, theassistant system may first learn a plurality of language-register modelscorresponding to a plurality of language registers associated with auser. When receiving the user input as part of an interaction withanother user or the assistant system, the assistant system may identifythe user's language register based on the user input and select asuitable language-register model accordingly. In addition, the assistantsystem may detect the change of the user's language register and respondto such change by selecting a different language-register model thatbetter fits the changed language register. The assistant system mayfurther use the selected language-register model in combination with alanguage template corresponding to the user's intent to generate thecommunication content. In particular embodiments, the generatedcommunication content may serve as a suggestion to the user for usage ifthe user is interacting with another user. The generated languagecommunication may also serve as a response to the user if the user isinteracting with the assistant system. By using particularlanguage-register models for particular language registers of a user,the generated communication contents are more natural and personalized.Although this disclosure describes generating particular communicationcontent via a particular system in a particular manner, this disclosurecontemplates generating any suitable communication content via anysuitable system in any suitable manner.

In particular embodiments, the assistant system may receive a first userinput by the first user from a client system associated with a firstuser. The first user input may be associated with a current dialogsession. In particular embodiments, the assistant system may identify afirst language register associated with the first user based on thefirst user input. In particular embodiments, the assistant system maythen access a plurality of language-register models associated with aplurality of language registers stored in a data store. The assistantsystem may select a first language-register model from the plurality oflanguage-register models based on the identified first languageregister. In particular embodiments, the assistant system may furthergenerate a first communication content responsive to the first userinput. The first communication content may be personalized for the firstuser based on the selected first language-register model.

Certain technical challenges exist for achieving the goal of generatinga particular communication content suitable for a particular intent anda particular language register. One technical challenge includesgenerating a particular communication content for a particular languageregister. The solution presented by the embodiments disclosed herein toaddress the above challenge is training various language-register modelscorresponding to various language registers based on a plurality ofgroups of training samples. Each group of training samples correspond toa particular language register. Another technical challenge includesgenerating a personalized and natural communication content for a user.The solution presented by the embodiments disclosed herein to addressthis challenge is training the language-register models based ontraining data associated with the user including news feed posts, newsfeed comments, user profile, messages, etc., associated with that user,allowing the language-register models to be personalized for the user.

Certain embodiments disclosed herein may provide one or more technicaladvantages. A technical advantage of the embodiments may includedynamically generating communication contents in different manners inresponse to different scenarios associated with users. For example, theassistant system may generate a formal and professional communicationcontent for a user who talks about a serious subject matter (e.g., apending lawsuit). Another technical advantage of the embodiments mayinclude increasing the degree of users engaging with the assistantsystem by providing users with more personalized and naturalcommunication contents. For example, long sentences with complicated orformal words may be unappealing to teenage users and they may not bewilling to use the assistant service in the future if the communicationcontents are based on such formality. By contrast, the assistant systemmay generate communication contents based on simple words/slang andshort sentences for teenage users to attract them to be more engagedwith the assistant system. Certain embodiments disclosed herein mayprovide none, some, or all of the above technical advantages. One ormore other technical advantages may be readily apparent to one skilledin the art in view of the figures, descriptions, and claims of thepresent disclosure.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed above.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with anassistant system.

FIG. 2 illustrates an example architecture of the assistant system.

FIG. 3 illustrates an example diagram flow of responding to a userrequest by the assistant system.

FIG. 4 illustrates an example diagram flow of generating a communicationcontent based on the example architecture of the assistant system inFIG. 2.

FIG. 5 illustrates an example method for generating a communicationcontent responsive to a user input.

FIG. 6 illustrates an example social graph.

FIG. 7 illustrates an example view of an embedding space.

FIG. 8 illustrates an example artificial neural network.

FIG. 9 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

System Overview

FIG. 1 illustrates an example network environment 100 associated with anassistant system. Network environment 100 includes a client system 130,an assistant system 140, a social-networking system 160, and athird-party system 170 connected to each other by a network 110.Although FIG. 1 illustrates a particular arrangement of a client system130, an assistant system 140, a social-networking system 160, athird-party system 170, and a network 110, this disclosure contemplatesany suitable arrangement of a client system 130, an assistant system140, a social-networking system 160, a third-party system 170, and anetwork 110. As an example and not by way of limitation, two or more ofa client system 130, a social-networking system 160, an assistant system140, and a third-party system 170 may be connected to each otherdirectly, bypassing a network 110. As another example, two or more of aclient system 130, an assistant system 140, a social-networking system160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130, assistantsystems 140, social-networking systems 160, third-party systems 170, andnetworks 110, this disclosure contemplates any suitable number of clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110. As an example and not by wayof limitation, network environment 100 may include multiple clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, an assistant system 140, asocial-networking system 160, and a third-party system 170 to acommunication network 110 or to each other. This disclosure contemplatesany suitable links 150. In particular embodiments, one or more links 150include one or more wireline (such as for example Digital SubscriberLine (DSL) or Data Over Cable Service Interface Specification (DOCSIS)),wireless (such as for example Wi-Fi or Worldwide Interoperability forMicrowave Access (WiMAX)), or optical (such as for example SynchronousOptical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links.In particular embodiments, one or more links 150 each include an ad hocnetwork, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN,a MAN, a portion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, smart speaker, other suitableelectronic device, or any suitable combination thereof. In particularembodiments, a client system 130 may be a smart assistant device. Moreinformation on smart assistant devices may be found in U.S. patentapplication Ser. No. 15/949,011, filed 9 Apr. 2018, U.S. PatentApplication No. 62/655,751, filed 10 Apr. 2018, U.S. patent applicationSer. No. 29/631,910, filed 3 Jan. 2018, U.S. patent application Ser. No.29/631,747, filed 2 Jan. 2018, U.S. patent application Ser. No.29/631,913, filed 3 Jan. 2018, and U.S. patent application Ser. No.29/631,914, filed 3 Jan. 2018, which are incorporated by reference. Thisdisclosure contemplates any suitable client systems 130. A client system130 may enable a network user at a client system 130 to access a network110. A client system 130 may enable its user to communicate with otherusers at other client systems 130.

In particular embodiments, a client system 130 may include a web browser132 and may have one or more add-ons, plug-ins, or other extensions. Auser at a client system 130 may enter a Uniform Resource Locator (URL)or other address directing a web browser 132 to a particular server(such as server 162, or a server associated with a third-party system170), and the web browser 132 may generate a Hyper Text TransferProtocol (HTTP) request and communicate the HTTP request to server. Theserver may accept the HTTP request and communicate to a client system130 one or more Hyper Text Markup Language (HTML) files responsive tothe HTTP request. The client system 130 may render a web interface (e.g.a webpage) based on the HTML files from the server for presentation tothe user. This disclosure contemplates any suitable source files. As anexample and not by way of limitation, a web interface may be renderedfrom HTML files, Extensible Hyper Text Markup Language (XHTML) files, orExtensible Markup Language (XML) files, according to particular needs.Such interfaces may also execute scripts, combinations of markuplanguage and scripts, and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, a client system 130 may include asocial-networking application 134 installed on the client system 130. Auser at a client system 130 may use the social-networking application134 to access on online social network. The user at the client system130 may use the social-networking application 134 to communicate withthe user's social connections (e.g., friends, followers, followedaccounts, contacts, etc.). The user at the client system 130 may alsouse the social-networking application 134 to interact with a pluralityof content objects (e.g., posts, news articles, ephemeral content, etc.)on the online social network. As an example and not by way oflimitation, the user may browse trending topics and breaking news usingthe social-networking application 134.

In particular embodiments, a client system 130 may include an assistantapplication 136. A user at a client system 130 may use the assistantapplication 136 to interact with the assistant system 140. In particularembodiments, the assistant application 136 may comprise a stand-aloneapplication. In particular embodiments, the assistant application 136may be integrated into the social-networking application 134 or anothersuitable application (e.g., a messaging application). In particularembodiments, the assistant application 136 may be also integrated intothe client system 130, an assistant hardware device, or any othersuitable hardware devices. In particular embodiments, the assistantapplication 136 may be accessed via the web browser 132. In particularembodiments, the user may provide input via different modalities. As anexample and not by way of limitation, the modalities may include audio,text, image, video, etc. The assistant application 136 may communicatethe user input to the assistant system 140. Based on the user input, theassistant system 140 may generate responses. The assistant system 140may send the generated responses to the assistant application 136. Theassistant application 136 may then present the responses to the user atthe client system 130. The presented responses may be based on differentmodalities such as audio, text, image, and video. As an example and notby way of limitation, the user may verbally ask the assistantapplication 136 about the traffic information (i.e., via an audiomodality). The assistant application 136 may then communicate therequest to the assistant system 140. The assistant system 140 mayaccordingly generate the result and send it back to the assistantapplication 136. The assistant application 136 may further present theresult to the user in text.

In particular embodiments, an assistant system 140 may assist users toretrieve information from different sources. The assistant system 140may also assist user to request services from different serviceproviders. In particular embodiments, the assist system 140 may receivea user request for information or services via the assistant application136 in the client system 130. The assist system 140 may usenatural-language understanding to analyze the user request based on userprofile and other relevant information. The result of the analysis maycomprise different entities associated with an online social network.The assistant system 140 may then retrieve information or requestservices associated with these entities. In particular embodiments, theassistant system 140 may interact with the social-networking system 160and/or third-party system 170 when retrieving information or requestingservices for the user. In particular embodiments, the assistant system140 may generate a personalized communication content for the user usingnatural-language generating techniques. The personalized communicationcontent may comprise, for example, the retrieved information or thestatus of the requested services. In particular embodiments, theassistant system 140 may enable the user to interact with it regardingthe information or services in a stateful and multi-turn conversation byusing dialog-management techniques. The functionality of the assistantsystem 140 is described in more detail in the discussion of FIG. 2below.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user-profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, or a third-party system 170 to manage, retrieve, modify,add, or delete, the information stored in data store 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow athird-party system 170 to access information from the social-networkingsystem 160 by calling one or more APIs. An action logger may be used toreceive communications from a web server about a user's actions on oroff the social-networking system 160. In conjunction with the actionlog, a third-party-content-object log may be maintained of userexposures to third-party-content objects. A notification controller mayprovide information regarding content objects to a client system 130.Information may be pushed to a client system 130 as notifications, orinformation may be pulled from a client system 130 responsive to arequest received from a client system 130. Authorization servers may beused to enforce one or more privacy settings of the users of thesocial-networking system 160. A privacy setting of a user determines howparticular information associated with a user can be shared. Theauthorization server may allow users to opt in to or opt out of havingtheir actions logged by the social-networking system 160 or shared withother systems (e.g., a third-party system 170), such as, for example, bysetting appropriate privacy settings. Third-party-content-object storesmay be used to store content objects received from third parties, suchas a third-party system 170. Location stores may be used for storinglocation information received from client systems 130 associated withusers. Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

Assistant Systems

FIG. 2 illustrates an example architecture of the assistant system 140.In particular embodiments, the assistant system 140 may assist a user toobtain information or services. The assistant system 140 may enable theuser to interact with it with multi-modal user input (such as voice,text, image, video) in stateful and multi-turn conversations to getassistance. The assistant system 140 may create and store a user profilecomprising both personal and contextual information associated with theuser. In particular embodiments, the assistant system 140 may analyzethe user input using natural-language understanding. The analysis may bebased on the user profile for more personalized and context-awareunderstanding. The assistant system 140 may resolve entities associatedwith the user input based on the analysis. In particular embodiments,the assistant system 140 may interact with different agents to obtaininformation or services that are associated with the resolved entities.The assistant system 140 may generate a response for the user regardingthe information or services by using natural-language generation.Through the interaction with the user, the assistant system 140 may usedialog management techniques to manage and forward the conversation flowwith the user. In particular embodiments, the assistant system 140 mayfurther assist the user to effectively and efficiently digest theobtained information by summarizing the information. The assistantsystem 140 may also assist the user to be more engaging with an onlinesocial network by providing tools that help the user interact with theonline social network (e.g., creating posts, comments, messages). Theassistant system 140 may additionally assist the user to managedifferent tasks such as keeping track of events. In particularembodiments, the assistant system 140 may proactively executepre-authorized tasks that are relevant to user interests and preferencesbased on the user profile, at a time relevant for the user, without auser input. In particular embodiments, the assistant system 140 maycheck privacy settings whenever it is necessary to guarantee thataccessing user profile and executing different tasks are subject to theuser's privacy settings.

In particular embodiments, the assistant system 140 may receive a userinput from the assistant application 136 in the client system 130associated with the user. If the user input is based on a text modality,the assistant system 140 may receive it at a messaging platform 205. Ifthe user input is based on an audio modality (e.g., the user may speakto the assistant application 136 or send a video including speech to theassistant application 136), the assistant system 140 may process itusing an audio speech recognition (ASR) module 210 to convert the userinput into text. If the user input is based on an image or videomodality, the assistant system 140 may process it using opticalcharacter recognition techniques within the messaging platform 205 toconvert the user input into text. The output of the messaging platform205 or the ASR module 210 may be received at an assistant xbot 215.

In particular embodiments, the assistant xbot 215 may be a type of chatbot. The assistant xbot 215 may comprise a programmable service channel,which may be a software code, logic, or routine that functions as apersonal assistant to the user. The assistant xbot 215 may work as theuser's portal to the assistant system 140. The assistant xbot 215 maytherefore be considered as a type of conversational agent. In particularembodiments, the assistant xbot 215 may send the textual user input to anatural-language understanding (NLU) module 220 to interpret the userinput. In particular embodiments, the NLU module 220 may get informationfrom a user context engine 225 and a semantic information aggregator 230to accurately understand the user input. The user context engine 225 maystore the user profile of the user. The user profile of the user maycomprise user-profile data including demographic information, socialinformation, and contextual information associated with the user. Theuser-profile data may also include user interests and preferences on aplurality of topics, aggregated through conversations on news feed,search logs, messaging platform 205, etc. The usage of user profile maybe protected behind a privacy check module 245 to ensure that a user'sinformation can be used only for his/her benefit, and not shared withanyone else. The semantic information aggregator 230 may provideontology data associated with a plurality of predefined domains,intents, and slots to the NLU module 220. In particular embodiments, adomain may denote a social context of interaction, e.g., education. Anintent may indicate a purpose of a user interacting with the assistantsystem 140. A slot may represent a basic semantic entity. For example, aslot for “pizza” may be dish. The semantic information aggregator 230may additionally extract information from a social graph, a knowledgegraph, and a concept graph, and retrieve user profile from the usercontext engine 225. The semantic information aggregator 230 may furtherprocess information from these different sources by determining whatinformation to aggregate, annotating n-grams of the user input, rankingthe n-grams with confidence scores based on the aggregated information,formulating the ranked n-grams into features that can be used by the NLUmodule 220 for understanding the user input. Based on the output of theuser context engine 225 and the semantic information aggregator 230, theNLU module 220 may identify a domain, an intent, and one or more slotsfrom the user input in a personalized and context-aware manner. Inparticular embodiments, the NLU module 220 may comprise a lexicon oflanguage and a parser and grammar rules to partition sentences into aninternal representation. The NLU module 220 may also comprise one ormore programs that perform naive semantics or stochastic semanticanalysis to the use of pragmatics to understand a user input. Inparticular embodiments, the parser may be based on a deep learningarchitecture comprising multiple long-short term memory (LSTM) networks.As an example and not by way of limitation, the parser may be based on arecurrent neural network grammar (RNNG) model, which is a type ofrecurrent and recursive LSTM algorithm.

In particular embodiments, the identified domain, intent, and one ormore slots from the NLU module 220 may be sent to a dialog engine 235.In particular embodiments, the dialog engine 235 may manage the dialogstate and flow of the conversation between the user and the assistantxbot 215. The dialog engine 235 may additionally store previousconversations between the user and the assistant xbot 215. In particularembodiments, the dialog engine 235 may communicate with an entityresolution module 240 to resolve entities associated with the one ormore slots, which supports the dialog engine 235 to forward the flow ofthe conversation between the user and the assistant xbot 215. Inparticular embodiments, the entity resolution module 240 may access thesocial graph, the knowledge graph, and the concept graph when resolvingthe entities. Entities may include, for example, unique users orconcepts, each of which may have a unique identifier (ID). As an exampleand not by way of limitation, the knowledge graph may comprise aplurality of entities. Each entity may comprise a single recordassociated with one or more attribute values. The particular record maybe associated with a unique entity identifier. Each record may havediverse values for an attribute of the entity. Each attribute value maybe associated with a confidence probability. A confidence probabilityfor an attribute value represents a probability that the value isaccurate for the given attribute. Each attribute value may be alsoassociated with a semantic weight. A semantic weight for an attributevalue may represent how the value semantically appropriate for the givenattribute considering all the available information. For example, theknowledge graph may comprise an entity of a movie “The Martian” (2015),which includes information that has been extracted from multiple contentsources, and then deduped, resolved, and fused to generate the singleunique record for the knowledge graph. The entity may be associated witha space attribute value which indicates the genre of the movie “TheMartian” (2015). The entity resolution module 240 may additionallyrequest user profile of the user associated with the user input from theuser context engine 225. In particular embodiments, the entityresolution module 240 may communicate with a privacy check module 245 toguarantee that the resolving of the entities does not violate privacypolicies. In particular embodiments, the privacy check module 245 mayuse an authorization/privacy server to enforce privacy policies. As anexample and not by way of limitation, an entity to be resolved may beanother user who specifies in his/her privacy settings that his/heridentity should not be searchable on the online social network, and thusthe entity resolution module 240 may not return that user's identifierin response to a request. Based on the information obtained from thesocial graph, knowledge graph, concept graph, and user profile, andsubject to applicable privacy policies, the entity resolution module 240may therefore accurately resolve the entities associated with the userinput in a personalized and context-aware manner. In particularembodiments, each of the resolved entities may be associated with one ormore identifiers hosted by the social-networking system 160. As anexample and not by way of limitation, an identifier may comprise aunique user identifier (ID). In particular embodiments, each of theresolved entities may be also associated with a confidence score.

In particular embodiments, the dialog engine 235 may communicate withdifferent agents based on the identified intent and domain, and theresolved entities. In particular embodiments, the agents may comprisefirst-party agents 250 and third-party agents 255. In particularembodiments, first-party agents 250 may comprise internal agents thatare accessible and controllable by the assistant system 140 (e.g. agentsassociated with services provided by the online social network). Inparticular embodiments, third-party agents 255 may comprise externalagents that the assistant system 140 has no control over (e.g., musicstreams agents, ticket sales agents). The first-party agents 250 may beassociated with first-party providers 260 that provide content objectsand/or services hosted by the social-networking system 160. Thethird-party agents 255 may be associated with third-party providers 265that provide content objects and/or services hosted by the third-partysystem 170.

In particular embodiments, the communication from the dialog engine 235to the first-party agents 250 may comprise requesting particular contentobjects and/or services provided by the first-party providers 260. As aresult, the first-party agents 250 may retrieve the requested contentobjects from the first-party providers 260 and/or execute tasks thatcommand the first-party providers 260 to perform the requested services.In particular embodiments, the communication from the dialog engine 235to the third-party agents 255 may comprise requesting particular contentobjects and/or services provided by the third-party providers 265. As aresult, the third-party agents 255 may retrieve the requested contentobjects from the third-party providers 265 and/or execute tasks thatcommand the third-party providers 265 to perform the requested services.The third-party agents 255 may access the privacy check module 245 toguarantee no privacy violations before interacting with the third-partyproviders 265. As an example and not by way of limitation, the userassociated with the user input may specify in his/her privacy settingsthat his/her profile information is invisible to any third-party contentproviders. Therefore, when retrieving content objects associated withthe user input from the third-party providers 265, the third-partyagents 255 may complete the retrieval without revealing to thethird-party providers 265 which user is requesting the content objects.

In particular embodiments, each of the first-party agents 250 orthird-party agents 255 may be designated for a particular domain. As anexample and not by way of limitation, the domain may comprise weather,transportation, music, etc. In particular embodiments, the assistantsystem 140 may use a plurality of agents collaboratively to respond to auser input. As an example and not by way of limitation, the user inputmay comprise “direct me to my next meeting.” The assistant system 140may use a calendar agent to retrieve the location of the next meeting.The assistant system 140 may then use a navigation agent to direct theuser to the next meeting.

In particular embodiments, each of the first-party agents 250 orthird-party agents 255 may retrieve user profile from the user contextengine 225 to execute tasks in a personalized and context-aware manner.As an example and not by way of limitation, a user input may comprise“book me a ride to the airport.” A transportation agent may execute thetask of booking the ride. The transportation agent may retrieve userprofile of the user from the user context engine 225 before booking theride. For example, the user profile may indicate that the user preferstaxis, so the transportation agent may book a taxi for the user. Asanother example, the contextual information associated with the userprofile may indicate that the user is in a hurry so the transportationagent may book a ride from a ride-sharing service for the user since itmay be faster to get a car from a ride-sharing service than a taxicompany. In particular embodiment, each of the first-party agents 250 orthird-party agents 255 may take into account other factors whenexecuting tasks. As an example and not by way of limitation, otherfactors may comprise price, rating, efficiency, partnerships with theonline social network, etc.

In particular embodiments, the dialog engine 235 may communicate with aconversational understanding composer (CU composer) 270. The dialogengine 235 may send the requested content objects and/or the statuses ofthe requested services to the CU composer 270. In particularembodiments, the dialog engine 235 may send the requested contentobjects and/or the statuses of the requested services as a <k, c, u, d>tuple, in which k indicates a knowledge source, c indicates acommunicative goal, u indicates a user model, and d indicates adiscourse model. In particular embodiments, the CU composer 270 maycomprise a natural-language generator (NLG) 271 and a user interface(UI) payload generator 272. The natural-language generator 271 maygenerate a communication content based on the output of the dialogengine 235. In particular embodiments, the NLG 271 may comprise acontent determination component, a sentence planner, and a surfacerealization component. The content determination component may determinethe communication content based on the knowledge source, communicativegoal, and the user's expectations. As an example and not by way oflimitation, the determining may be based on a description logic. Thedescription logic may comprise, for example, three fundamental notionswhich are individuals (representing objects in the domain), concepts(describing sets of individuals), and roles (representing binaryrelations between individuals or concepts). The description logic may becharacterized by a set of constructors that allow the natural-languagegenerator 271 to build complex concepts/roles from atomic ones. Inparticular embodiments, the content determination component may performthe following tasks to determine the communication content. The firsttask may comprise a translation task, in which the input to thenatural-language generator 271 may be translated to concepts. The secondtask may comprise a selection task, in which relevant concepts may beselected among those resulted from the translation task based on theuser model. The third task may comprise a verification task, in whichthe coherence of the selected concepts may be verified. The fourth taskmay comprise an instantiation task, in which the verified concepts maybe instantiated as an executable file that can be processed by thenatural-language generator 271. The sentence planner may determine theorganization of the communication content to make it humanunderstandable. The surface realization component may determine specificwords to use, the sequence of the sentences, and the style of thecommunication content. The UI payload generator 272 may determine apreferred modality of the communication content to be presented to theuser. In particular embodiments, the CU composer 270 may communicatewith the privacy check module 245 to make sure the generation of thecommunication content follows the privacy policies. In particularembodiments, the CU composer 270 may retrieve user profile from the usercontext engine 225 when generating the communication content anddetermining the modality of the communication content. As a result, thecommunication content may be more natural, personalized, andcontext-aware for the user. As an example and not by way of limitation,the user profile may indicate that the user likes short sentences inconversations so the generated communication content may be based onshort sentences. As another example and not by way of limitation, thecontextual information associated with the user profile may indicatedthat the user is using a device that only outputs audio signals so theUI payload generator 272 may determine the modality of the communicationcontent as audio.

In particular embodiments, the CU composer 270 may send the generatedcommunication content to the assistant xbot 215. In particularembodiments, the assistant xbot 215 may send the communication contentto the messaging platform 205. The messaging platform 205 may furthersend the communication content to the client system 130 via theassistant application 136. In alternative embodiments, the assistantxbot 215 may send the communication content to a text-to-speech (TTS)module 275. The TTS module 275 may convert the communication content toan audio clip. The TTS module 275 may further send the audio clip to theclient system 130 via the assistant application 136.

In particular embodiments, the assistant xbot 215 may interact with aproactive inference layer 280 without receiving a user input. Theproactive inference layer 280 may infer user interests and preferencesbased on user profile that is retrieved from the user context engine225. In particular embodiments, the proactive inference layer 280 mayfurther communicate with proactive agents 285 regarding the inference.The proactive agents 285 may execute proactive tasks based on theinference. As an example and not by way of limitation, the proactivetasks may comprise sending content objects or providing services to theuser. In particular embodiments, each proactive task may be associatedwith an agenda item. The agenda item may comprise a recurring item suchas a daily digest. The agenda item may also comprise a one-time item. Inparticular embodiments, a proactive agent 285 may retrieve user profilefrom the user context engine 225 when executing the proactive task.Therefore, the proactive agent 285 may execute the proactive task in apersonalized and context-aware manner. As an example and not by way oflimitation, the proactive inference layer may infer that the user likesthe band Maroon 5 and the proactive agent 285 may generate arecommendation of Maroon 5's new song/album to the user.

In particular embodiments, the proactive agent 285 may generatecandidate entities associated with the proactive task based on userprofile. The generation may be based on a straightforward backend queryusing deterministic filters to retrieve the candidate entities from astructured data store. The generation may be alternatively based on amachine-learning model that is trained based on user profile, entityattributes, and relevance between users and entities. As an example andnot by way of limitation, the machine-learning model may be based onsupport vector machines (SVM). As another example and not by way oflimitation, the machine-learning model may be based on a regressionmodel. As another example and not by way of limitation, themachine-learning model may be based on a deep convolutional neuralnetwork (DCNN). In particular embodiments, the proactive agent 285 mayalso rank the generated candidate entities based on user profile and thecontent associated with the candidate entities. The ranking may be basedon the similarities between a user's interests and the candidateentities. As an example and not by way of limitation, the assistantsystem 140 may generate a feature vector representing a user's interestand feature vectors representing the candidate entities. The assistantsystem 140 may then calculate similarity scores (e.g., based on cosinesimilarity) between the feature vector representing the user's interestand the feature vectors representing the candidate entities. The rankingmay be alternatively based on a ranking model that is trained based onuser feedback data.

In particular embodiments, the proactive task may comprise recommendingthe candidate entities to a user. The proactive agent 285 may schedulethe recommendation, thereby associating a recommendation time with therecommended candidate entities. The recommended candidate entities maybe also associated with a priority and an expiration time. In particularembodiments, the recommended candidate entities may be sent to aproactive scheduler. The proactive scheduler may determine an actualtime to send the recommended candidate entities to the user based on thepriority associated with the task and other relevant factors (e.g.,clicks and impressions of the recommended candidate entities). Inparticular embodiments, the proactive scheduler may then send therecommended candidate entities with the determined actual time to anasynchronous tier. The asynchronous tier may temporarily store therecommended candidate entities as a job. In particular embodiments, theasynchronous tier may send the job to the dialog engine 235 at thedetermined actual time for execution. In alternative embodiments, theasynchronous tier may execute the job by sending it to other surfaces(e.g., other notification services associated with the social-networkingsystem 160). In particular embodiments, the dialog engine 235 mayidentify the dialog intent, state, and history associated with the user.Based on the dialog intent, the dialog engine 235 may select somecandidate entities among the recommended candidate entities to send tothe client system 130. In particular embodiments, the dialog state andhistory may indicate if the user is engaged in an ongoing conversationwith the assistant xbot 215. If the user is engaged in an ongoingconversation and the priority of the task of recommendation is low, thedialog engine 235 may communicate with the proactive scheduler toreschedule a time to send the selected candidate entities to the clientsystem 130. If the user is engaged in an ongoing conversation and thepriority of the task of recommendation is high, the dialog engine 235may initiate a new dialog session with the user in which the selectedcandidate entities may be presented. As a result, the interruption ofthe ongoing conversation may be prevented. When it is determined thatsending the selected candidate entities is not interruptive to the user,the dialog engine 235 may send the selected candidate entities to the CUcomposer 270 to generate a personalized and context-aware communicationcontent comprising the selected candidate entities, subject to theuser's privacy settings. In particular embodiments, the CU composer 270may send the communication content to the assistant xbot 215 which maythen send it to the client system 130 via the messaging platform 205 orthe TTS module 275.

In particular embodiments, the assistant xbot 215 may communicate with aproactive agent 285 in response to a user input. As an example and notby way of limitation, the user may ask the assistant xbot 215 to set upa reminder. The assistant xbot 215 may request a proactive agent 285 toset up such reminder and the proactive agent 285 may proactively executethe task of reminding the user at a later time.

In particular embodiments, the assistant system 140 may comprise asummarizer 290. The summarizer 290 may provide customized news feedsummaries to a user. In particular embodiments, the summarizer 290 maycomprise a plurality of meta agents. The plurality of meta agents mayuse the first-party agents 250, third-party agents 255, or proactiveagents 285 to generated news feed summaries. In particular embodiments,the summarizer 290 may retrieve user interests and preferences from theproactive inference layer 280. The summarizer 290 may then retrieveentities associated with the user interests and preferences from theentity resolution module 240. The summarizer 290 may further retrieveuser profile from the user context engine 225. Based on the informationfrom the proactive inference layer 280, the entity resolution module240, and the user context engine 225, the summarizer 290 may generatepersonalized and context-aware summaries for the user. In particularembodiments, the summarizer 290 may send the summaries to the CUcomposer 270. The CU composer 270 may process the summaries and send theprocessing results to the assistant xbot 215. The assistant xbot 215 maythen send the processed summaries to the client system 130 via themessaging platform 205 or the TTS module 275.

FIG. 3 illustrates an example diagram flow of responding to a userrequest by the assistant system 140. In particular embodiments, theassistant xbot 215 may access a request manager 305 upon receiving theuser request. The request manager 305 may comprise a context extractor306 and a conversational understanding object generator (CU objectgenerator) 307. The context extractor 306 may extract contextualinformation associated with the user request. The CU object generator307 may generate particular content objects relevant to the userrequest. In particular embodiments, the request manager 305 may storethe contextual information and the generated content objects in datastore 310 which is a particular data store implemented in the assistantsystem 140.

In particular embodiments, the request manger 305 may send the generatedcontent objects to the NLU module 220. The NLU module 220 may perform aplurality of steps to process the content objects. At step 221, the NLUmodule 220 may generate a whitelist for the content objects. At step222, the NLU module 220 may perform a featurization based on thewhitelist. At step 223, the NLU module 220 may perform domainclassification/selection based on the features resulted from thefeaturization. The domain classification/selection results may befurther processed based on two related procedures. At step 224 a, theNLU module 220 may process the domain classification/selection resultusing an intent classifier. The intent classifier may determine theuser's intent associated with the user request. As an example and not byway of limitation, the intent classifier may be based on amachine-learning model that may take the domain classification/selectionresult as input and calculate a probability of the input beingassociated with a particular predefined intent. At step 224 b, the NLUmodule may process the domain classification/selection result using ameta-intent classifier. The meta-intent classifier may determinecategories that describe the user's intent. As an example and not by wayof limitation, the meta-intent classifier may be based on amachine-learning model that may take the domain classification/selectionresult as input and calculate a probability of the input beingassociated with a particular predefined meta-intent. At step 225 a, theNLU module 220 may use a slot tagger to annotate one or more slotsassociated with the user request. At step 225 b, the NLU module 220 mayuse a meta slot tagger to annotate one or more slots for theclassification result from the meta-intent classifier. As an example andnot by way of limitation, a user request may comprise “change 500dollars in my account to Japanese yen.” The intent classifier may takethe user request as input and formulate it into a vector. The intentclassifier may then calculate probabilities of the user request beingassociated with different predefined intents based on a vectorcomparison between the vector representing the user request and thevectors representing different predefined intents. In a similar manner,the slot tagger may take the user request as input and formulate eachword into a vector. The intent classifier may then calculateprobabilities of each word being associated with different predefinedslots based on a vector comparison between the vector representing theword and the vectors representing different predefined slots. The intentof the user may be classified as “changing money”. The slots of the userrequest may comprise “500”, “dollars”, “account”, and “Japanese yen”.The meta-intent of the user may be classified as “financial service”.The meta slot may comprise “finance”.

In particular embodiments, the NLU module 220 may improve the domainclassification/selection of the content objects by extracting semanticinformation from the semantic information aggregator 230. In particularembodiments, the semantic information aggregator 230 may aggregatesemantic information in the following way. The semantic informationaggregator 230 may first retrieve information from the user contextengine 225. In particular embodiments, the user context engine 225 maycomprise offline aggregators 226 and an online inference service 227.The offline aggregators 226 may process a plurality of data associatedwith the user that are collected from a prior time window. As an exampleand not by way of limitation, the data may include news feedposts/comments, interactions with news feed posts/comments, searchhistory, etc. that are collected from a prior 90-day window. Theprocessing result may be stored in the user context engine 225 as partof the user profile. The online inference service 227 may analyze theconversational data associated with the user that are received by theassistant system 140 at a current time. The analysis result may bestored in the user context engine 225 also as part of the user profile.In particular embodiments, the semantic information aggregator 230 maythen process the retrieved information, i.e., user profile, from theuser context engine 225 in the following steps. At step 231, thesemantic information aggregator 230 may process the retrievedinformation from the user context engine 225 based on natural-languageprocessing (NLP). At step 232, the processing result may be annotatedwith entities by an entity tagger. Based on the annotations, thesemantic information aggregator 230 may generate dictionaries for theretrieved information at step 233. At step 234, the semantic informationaggregator 230 may rank the entities tagged by the entity tagger. Inparticular embodiments, the semantic information aggregator 230 maycommunicate with different graphs 330 including social graph, knowledgegraph, and concept graph to extract ontology data that is relevant tothe retrieved information from the user context engine 225. Inparticular embodiments, the semantic information aggregator 230 mayaggregate user profile, the ranked entities, and the information fromthe graphs 330. The semantic information aggregator 230 may then sendthe aggregated information to the NLU module 220 to facilitate thedomain classification/selection.

In particular embodiments, the output of the NLU module 220 may be sentto a co-reference module 315 to interpret references of the contentobjects associated with the user request. The co-reference module 315may comprise reference creation 316 and reference resolution 317. Inparticular embodiments, the reference creation 316 may create referencesfor entities determined by the NLU module 220. The reference resolution317 may resolve these references accurately. In particular embodiments,the co-reference module 315 may access the user context engine 225 andthe dialog engine 235 when necessary to interpret references withimproved accuracy.

In particular embodiments, the identified domains, intents,meta-intents, slots, and meta slots, along with the resolved referencesmay be sent to the entity resolution module 240 to resolve relevantentities. In particular embodiments, the entity resolution module 240may comprise domain entity resolution 241 and generic entity resolution242. The domain entity resolution 241 may resolve the entities bycategorizing the slots and meta slots into different domains. Inparticular embodiments, entities may be resolved based on the ontologydata extracted from the graphs 330. The ontology data may comprise thestructural relationship between different slots/meta-slots and domains.The ontology may also comprise information of how the slots/meta-slotsmay be grouped, related within a hierarchy where the higher levelcomprises the domain, and subdivided according to similarities anddifferences. The generic entity resolution 242 may resolve the entitiesby categorizing the slots and meta slots into different generic topics.In particular embodiments, the resolving may be also based on theontology data extracted from the graphs 330. The ontology data maycomprise the structural relationship between different slots/meta-slotsand generic topics. The ontology may also comprise information of howthe slots/meta-slots may be grouped, related within a hierarchy wherethe higher level comprises the topic, and subdivided according tosimilarities and differences. As an example and not by way oflimitation, in response to the input of an inquiry of the advantages ofa car, the generic entity resolution 242 may resolve the car as vehicleand the domain entity resolution 241 may resolve the car as electriccar.

In particular embodiments, the output of the entity resolution module240 may be sent to the dialog engine 235 to forward the flow of theconversation with the user. The dialog engine 235 may comprise dialogintent resolution 236 and dialog state update/ranker 237. In particularembodiments, the dialog intent resolution 236 may resolve the userintent associated with the current dialog session. In particularembodiments, the dialog state update/ranker 237 may update/rank thestate of the current dialog session. As an example and not by way oflimitation, the dialog state update/ranker 237 may update the dialogstate as “completed” if the dialog session is over. As another exampleand not by way of limitation, the dialog state update/ranker 237 mayrank the dialog state based on a priority associated with it.

In particular embodiments, the dialog engine 235 may communicate with atask completion module 335 about the dialog intent and associatedcontent objects. The task completion module 335 may comprise an actionselection component 336. In particular embodiments, the dialog engine235 may additionally check against dialog policies 320 regarding thedialog state. The dialog policies 320 may comprise generic policy 321and domain specific policies 322, both of which may guide how to selectthe next system action based on the dialog state. In particularembodiments, the task completion module 335 may communicate with dialogpolicies 320 to obtain the guidance of the next system action. Inparticular embodiments, the action selection component 336 may thereforeselect an action based on the dialog intent, the associated contentobjects, and the guidance from dialog policies 320.

In particular embodiments, the output of the task completion module 335may be sent to the CU composer 270. In alternative embodiments, theselected action may require one or more agents 340 to be involved. As aresult, the task completion module 335 may inform the agents 340 aboutthe selected action. Meanwhile, the dialog engine 235 may receive aninstruction to update the dialog state. As an example and not by way oflimitation, the update may comprise awaiting agents' response. Inparticular embodiments, the CU composer 270 may generate a communicationcontent for the user using the NLG 271 based on the output of the taskcompletion module 335. The CU composer 270 may also determine a modalityof the generated communication content using the UI payload generator272. Since the generated communication content may be considered as aresponse to the user request, the CU composer 270 may additionally rankthe generated communication content using a response ranker 273. As anexample and not by way of limitation, the ranking may indicate thepriority of the response.

In particular embodiments, the output of the CU composer 270 may be sentto a response manager 325. The response manager 325 may performdifferent tasks including storing/updating the dialog state 326retrieved from data store 310 and generating responses 327. Inparticular embodiments, the generated response and the communicationcontent may be sent to the assistant xbot 215. In alternativeembodiments, the output of the CU composer 270 may be additionally sentto the TTS module 275 if the determined modality of the communicationcontent is audio. The speech generated by the TTS module 275 and theresponse generated by the response manager 325 may be then sent to theassistant xbot 215.

Assisting Users with Personalized and Contextual Communication Content

In particular embodiments, an assistant system 140 may automaticallyidentify a user's intent and language register based on a user input,generate a communication content suitable for such intent and languageregister for the user, detect a change of the user's intent and languageregister, and dynamically adjust the generation of the communicationcontent to fit the changed intent and language register. In linguistics,a language register characterizes a particular intent coupled with aparticular scenario, which is a linguistic term of art for a variationof a language used for a particular purpose or in a particular socialsetting. For example, a language register may reflect word choices,voice tunes (in speech), speed, etc. In particular embodiments, theassistant system 140 may first learn a plurality of language-registermodels corresponding to a plurality of language registers associatedwith a user. When receiving the user input as part of an interactionwith another user or the assistant system 140, the assistant system 140may identify the user's language register based on the user input andselect a suitable language-register model accordingly. In addition, theassistant system 140 may detect the change of the user's languageregister and respond to such change by selecting a differentlanguage-register model that better fits the changed language register.The assistant system 140 may further use the selected language-registermodel in combination with a language template corresponding to theuser's intent to generate the communication content. In particularembodiments, the generated communication content may serve as asuggestion to the user for usage if the user is interacting with anotheruser. The generated language communication may also serve as a responseto the user if the user is interacting with the assistant system 140. Byusing particular language-register models for particular languageregisters of a user, the generated communication contents are morenatural and personalized. Although this disclosure describes generatingparticular communication content via a particular system in a particularmanner, this disclosure contemplates generating any suitablecommunication content via any suitable system in any suitable manner.

FIG. 4 illustrates an example diagram flow of generating a communicationcontent based on the example architecture 200 of the assistant system140 in FIG. 2. In particular embodiments, the assistant xbot 215 mayreceive a first user input 405 by a first user from a client system 130associated with the first user. The first user input 405 may beassociated with a current dialog session. The assistant xbot 215 maysend the first user input 405 to the NLU module 220. In particularembodiments, the NLU module 220 may identify a first language registerassociated with the first user based on the first user input 405. TheNLU module 220 may identify the first language register based on amachine-learning model. In particular embodiments, the NLU module 220may then access a plurality of language-register models associated witha plurality of language registers stored in a data store. As an exampleand not by way of limitation, the data store may comprise the usercontext engine 225. In particular embodiments, the NLU module 220 mayuse the machine-learning model to process the user input 405 to generatea feature vector, calculate probabilities of the user input 405 beingassociated with the plurality of language registers, and identify thefirst language register as the one with which the user input 405 has thehighest similarity. In particular embodiments, the NLU module 220 maysend the identified first language register to the user context engine225 when accessing the plurality of language-register models. Inparticular embodiments, the user context engine 225 may select a firstlanguage-register model from the plurality of language-register modelsbased on the identified first language register. The selectedlanguage-register model 415 may be sent to the CU composer 270. Inparticular embodiments, the NLU module 220 may further identify anintent and one or more slots associated with the first user input 405.As an example and not by way of limitation, the user input 405 may be“show me the showtime of spider man”. The intent may be gettinginformation of a movie. One slot may be <time> corresponding to“showtime” and another slot may be <name> corresponding to “spider man”.In particular embodiments, the NLU module 220 may use a deep learningarchitecture comprising multiple long-short term memory (LSTM) networksto identify the intent and the one or more slots. As an example and notby way of limitation, the NLU module 220 may use a recurrent neuralnetwork grammar (RNNG) model, which is a type of recurrent and recursiveLSTM algorithm, to identify the intent and slots. The NLU module 220 maysend the identified intent and slots 420 to the dialog engine 235. Inparticular embodiments, the dialog engine 235 may determine an actionbased on the identified intent and execute the action to retrieveinformation associated with the one or more slots from one or moreinformation sources. Continuing with the previous example, the actionmay be fetching movie information. In particular embodiments, the one ormore information sources may comprise one or more of: one or morefirst-party agents 250; or one or more third-party agents 255. Inparticular embodiments, the dialog engine 235 may send the retrievedinformation 425 along with the intent and slots 420 to the CU composer270. In particular embodiments, the CU composer 270 may access aplurality of language-generation templates stored in the data store(e.g., the user context engine 225). The plurality oflanguage-generation templates may be generated manually by human orautomatically by machine-learning algorithms. Each language-generationtemplate may comprise one or more slots corresponding to the one or moreslots associated with the first user input 405. The CU composer 270 maythen select a language-generation template from the plurality oflanguage-generation templates based on the action, which is determinedfrom the identified intent. The CU composer 270 may further generate afirst communication content 430 responsive to the first user input 405.The first communication content 430 may be personalized for the firstuser based on the selected first language-register model 415, the intentand slots 420, the retrieved information 425, and the selectedlanguage-generation template 430. In particular embodiments, thecommunication content 430 may be further sent from the CU composer 270to the assistant xbot 215. Although this disclosure describes generatingparticular communication content based on a particular process flow in aparticular manner, this disclosure contemplates generating any suitablecommunication content based on any suitable process flow in any suitablemanner.

In particular embodiments, the assistant system 140 may generate thecommunication content for the first user based on the following process.The assistant system 140 may generate a list of candidate words based onthe identified language register and the retrieved information. Eachcandidate word may be associated with a weight. As an example and not byway of limitation, the identified language register may comprise aformal language register and the list of candidate words may include“have not” instead of “haven't”. The assistant system 140 may then prunethe list of candidate words. As an example and not by way of limitation,the pruning may comprise removing junk words. The assistant system 140may also rank the pruned list of candidate words based on their weights.The assistant system 140 may further insert, from the pruned list ofcandidate words, one of the candidate words into each slot of theselected language-generation template. Although this disclosuredescribes generating particular communication content based onparticular candidate words in a particular manner, this disclosurecontemplates generating any suitable communication content based on anysuitable candidate words in any suitable manner.

In particular embodiments, the assistant system 140 may train theplurality of language-register models based on a word-embedding model.The assistant system 140 may train the plurality of language-registermodels offline and use them online. In particular embodiments, theword-embedding model may be based on convolutional neural networks. Asan example and not by way of limitation, the word-embedding model may beword2vec, which is a group of related models that are used to produceword embeddings. As another example and not by way of limitation, theword-embedding model may be FastText, which is an extension to word2vecthat breaks words into several n-grams instead of feeding individualwords into the neural networks. FastText generates word embeddings forall the n-grams after training the neural networks. Although thisdisclosure describes the word-embedding model being based onconvolutional neural networks, this disclosure contemplates theword-embedding model being based on any suitable model. In particularembodiments, the plurality of language-register models may bepersonalized with respect to the first user. The personalization may beachieved as follows. In particular embodiments, each language-registermodel may be trained based on a plurality of training samples associatedwith the first user. The training samples may comprise one or more ofnews feed posts, news feed comments, a user profile, or messages. Themessages may include the first user's chatting messages with theassistant xbot 215 and the usage of such messages is subject to thefirst user's privacy settings. The messages may also include the firstuser's chatting messages with one or more second users and the usage ofsuch messages is subject to both the first user's and second users'privacy settings. Training the language-register models based onaforementioned training data may be an effective solution for addressingthe technical challenge of generating a personalized and naturalcommunication content for a user. In addition, the embodiments disclosedherein may have a technical advantage of increasing the degree of usersengaging with the assistant system 140. For example, long sentences withcomplicated or formal words may be unappealing to teenage users and theymay not be willing to use the assistant service in the future if thecommunication contents are based on such formality. By contrast, theassistant system 140 may generate communication contents based on simplewords/slang and short sentences for teenage users to attract them to bemore engaged with the assistant system 140. In particular embodiments,the assistant system 140 may additionally use audio or video dataassociated with the first user to train the language-register model. Theassistant system 140 may use one or more techniques such as audio speechrecognition to extract textual data from the audio or video data. Inparticular embodiments, the assistant system 140 may further cluster theplurality of training samples into one or more groups of trainingsamples based on one or more criteria. In particular embodiments,different groups of training samples may correspond to differentlanguage registers. As an example and not by way of limitation, the oneor more criteria may comprise one or more of age, relationship,location, education, interest, native language, other suitable criteria,or any combination thereof. For example, a user's chatting messages withhis/her friends may form a group of training samples whereas the user'schatting messages with his/her family members may form another group oftraining samples. In particular embodiments, the assistant system 140may further train, for each group, a language-register model for thegroup based on the training samples associated with the group. Trainingvarious language-register models corresponding to various languageregisters based on a plurality of groups of training samples may be aneffective solution for addressing the technical challenge of generatinga particular communication content for a particular language register.Although this disclosure describes training particular language-registermodels in a particular manner, this disclosure contemplates training anysuitable language-register models in any suitable manner.

In particular embodiments, the first user may be in the current dialogsession with a second user. The current dialog session may comprise amessage thread between the first user and the second user. In particularembodiments, the assistant system 140 may send, to the client system130, instructions for presenting the generated first communicationcontent to the first user. The generated first communication content maybe operable to allow the first user to select the generated firstcommunication content. In particular embodiments, the assistant system140 may receive, from the client system 130, a selection of thegenerated first communication content from the first user. The assistantsystem 140 may then insert the generated first communication content inthe message thread between the first user and the second user. Inparticular embodiments, the first user may be in the current dialogsession with an assistant xbot 215. The current dialog session maycomprise a message thread between the first user and the assistant xbot215. In particular embodiments, the assistant system 140 may insert thegenerated first communication content in the message thread between thefirst user and the assistant xbot 215. Although this disclosuredescribes providing particular communication content to users inparticular manners, this disclosure contemplates providing any suitablecommunication content to users in any suitable manner.

In particular embodiments, the assistant system 140 may have a technicaladvantage of detecting a change of the language register associated withthe first user within the current dialog session and responding to suchchange by selecting a different language-register model that is moresuitable. In particular embodiments, the assistant system 140 mayreceive, from the client system 130, a second user input 405 by thefirst user. The second user input 405 may be associated with the currentdialog session. In particular embodiments, the assistant system 140 mayidentify a second language register associated with the first user basedon the second user input 405. The assistant system 140 may then accessthe plurality of language-register models associated with the pluralityof language registers stored in the data store. The assistant system 140may select a second language-register model from the plurality oflanguage-register models based on the identified second languageregister. In particular embodiments, the assistant system 140 mayfurther generate a second communication content responsive to the seconduser input 405. The second communication content may be personalized forthe first user based on the selected second language-register model. Asan example and not by way of limitation, the user may be talking withhis/her friend about going to a party at the beginning of theconversation but may switch to talking about applying for a job later inthe conversation, which may lead to a change of the language register.The assistant system 140 may be able to detect such change anddynamically select another language-register model to generate acommunication content that the user would use. Although this disclosuredescribes responding to a changed language register in a particularmanner, this disclosure contemplates responding to any suitable languageregisters in any suitable manner.

In particular embodiments, the assistant system 140 may supportmulti-modal user input and multi-modal output to the user. As an exampleand not by way of limitation, the first user input 405 may comprise oneor more of a character string, an audio clip, an image, a video, othersuitable input, or any combination thereof. For example, the user mayspeak to the assistant system 140. As another example and not by way oflimitation, the generated first communication content may comprise oneor more of a character string, an audio clip, an image, a video, othersuitable input, or any combination thereof. For example, the assistantsystem 140 may generate an audio message and read aloud to the user.Although this disclosure describes supporting particular modalities in aparticular manner, this disclosure contemplates supporting any suitablemodalities in any suitable manner.

FIG. 5 illustrates an example method 500 for generating a communicationcontent responsive to a user input 405. The method may begin at step510, where the assistant system 140 may receive, from a client system130 associated with a first user, a first user input 405 by the firstuser, wherein the first user input 405 is associated with a currentdialog session. At step 520, the assistant system 140 may identify afirst language register associated with the first user based on thefirst user input 405. At step 530, the assistant system 140 may access aplurality of language-register models associated with a plurality oflanguage registers stored in a data store. At step 540, the assistantsystem 140 may select a first language-register model from the pluralityof language-register models based on the identified first languageregister. At step 550, the assistant system 140 may generate a firstcommunication content responsive to the first user input 405, the firstcommunication content being personalized for the first user based on theselected first language-register model. Particular embodiments mayrepeat one or more steps of the method of FIG. 5, where appropriate.Although this disclosure describes and illustrates particular steps ofthe method of FIG. 5 as occurring in a particular order, this disclosurecontemplates any suitable steps of the method of FIG. 5 occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for generating a communication contentresponsive to a user input, including the particular steps of the methodof FIG. 5, this disclosure contemplates any suitable method forgenerating a communication content responsive to a user input, includingany suitable steps, which may include all, some, or none of the steps ofthe method of FIG. 5, where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 5, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 5.

Social Graphs

FIG. 6 illustrates an example social graph 600. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 600 in one or more data stores. In particular embodiments,the social graph 600 may include multiple nodes—which may includemultiple user nodes 602 or multiple concept nodes 604—and multiple edges606 connecting the nodes. Each node may be associated with a uniqueentity (i.e., user or concept), each of which may have a uniqueidentifier (ID), such as a unique number or username. The example socialgraph 600 illustrated in FIG. 6 is shown, for didactic purposes, in atwo-dimensional visual map representation. In particular embodiments, asocial-networking system 160, a client system 130, an assistant system140, or a third-party system 170 may access the social graph 600 andrelated social-graph information for suitable applications. The nodesand edges of the social graph 600 may be stored as data objects, forexample, in a data store (such as a social-graph database). Such a datastore may include one or more searchable or queryable indexes of nodesor edges of the social graph 600.

In particular embodiments, a user node 602 may correspond to a user ofthe social-networking system 160 or the assistant system 140. As anexample and not by way of limitation, a user may be an individual (humanuser), an entity (e.g., an enterprise, business, or third-partyapplication), or a group (e.g., of individuals or entities) thatinteracts or communicates with or over the social-networking system 160or the assistant system 140. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 602 corresponding tothe user, and store the user node 602 in one or more data stores. Usersand user nodes 602 described herein may, where appropriate, refer toregistered users and user nodes 602 associated with registered users. Inaddition or as an alternative, users and user nodes 602 described hereinmay, where appropriate, refer to users that have not registered with thesocial-networking system 160. In particular embodiments, a user node 602may be associated with information provided by a user or informationgathered by various systems, including the social-networking system 160.As an example and not by way of limitation, a user may provide his orher name, profile picture, contact information, birth date, sex, maritalstatus, family status, employment, education background, preferences,interests, or other demographic information. In particular embodiments,a user node 602 may be associated with one or more data objectscorresponding to information associated with a user. In particularembodiments, a user node 602 may correspond to one or more webinterfaces.

In particular embodiments, a concept node 604 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node604 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160 and the assistant system 140. As an exampleand not by way of limitation, information of a concept may include aname or a title; one or more images (e.g., an image of the cover page ofa book); a location (e.g., an address or a geographical location); awebsite (which may be associated with a URL); contact information (e.g.,a phone number or an email address); other suitable concept information;or any suitable combination of such information. In particularembodiments, a concept node 604 may be associated with one or more dataobjects corresponding to information associated with concept node 404.In particular embodiments, a concept node 404 may correspond to one ormore web interfaces.

In particular embodiments, a node in the social graph 600 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160 or the assistant system 140. Profileinterfaces may also be hosted on third-party websites associated with athird-party system 170. As an example and not by way of limitation, aprofile interface corresponding to a particular external web interfacemay be the particular external web interface and the profile interfacemay correspond to a particular concept node 604. Profile interfaces maybe viewable by all or a selected subset of other users. As an exampleand not by way of limitation, a user node 602 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 404 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 604.

In particular embodiments, a concept node 604 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject (which may be implemented, for example, in JavaScript, AJAX, orPHP codes) representing an action or activity. As an example and not byway of limitation, a third-party web interface may include a selectableicon such as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 402 corresponding to the user and a conceptnode 604 corresponding to the third-party web interface or resource andstore edge 606 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 600 maybe connected to each other by one or more edges 606. An edge 606connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 606 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 606 connecting the first user's user node 602 to thesecond user's user node 602 in the social graph 600 and store edge 606as social-graph information in one or more of data stores 166. In theexample of FIG. 6, the social graph 600 includes an edge 606 indicatinga friend relation between user nodes 602 of user “A” and user “B” and anedge indicating a friend relation between user nodes 602 of user “C” anduser “B.” Although this disclosure describes or illustrates particularedges 606 with particular attributes connecting particular user nodes602, this disclosure contemplates any suitable edges 606 with anysuitable attributes connecting user nodes 602. As an example and not byway of limitation, an edge 606 may represent a friendship, familyrelationship, business or employment relationship, fan relationship(including, e.g., liking, etc.), follower relationship, visitorrelationship (including, e.g., accessing, viewing, checking-in, sharing,etc.), subscriber relationship, superior/subordinate relationship,reciprocal relationship, non-reciprocal relationship, another suitabletype of relationship, or two or more such relationships. Moreover,although this disclosure generally describes nodes as being connected,this disclosure also describes users or concepts as being connected.Herein, references to users or concepts being connected may, whereappropriate, refer to the nodes corresponding to those users or conceptsbeing connected in the social graph 600 by one or more edges 606.

In particular embodiments, an edge 606 between a user node 602 and aconcept node 606 may represent a particular action or activity performedby a user associated with user node 602 toward a concept associated witha concept node 604. As an example and not by way of limitation, asillustrated in FIG. 6, a user may “like,” “attended,” “played,”“listened,” “cooked,” “worked at,” or “watched” a concept, each of whichmay correspond to an edge type or subtype. A concept-profile interfacecorresponding to a concept node 604 may include, for example, aselectable “check in” icon (such as, for example, a clickable “check in”icon) or a selectable “add to favorites” icon. Similarly, after a userclicks these icons, the social-networking system 160 may create a“favorite” edge or a “check in” edge in response to a user's actioncorresponding to a respective action. As another example and not by wayof limitation, a user (user “C”) may listen to a particular song(“Imagine”) using a particular application (an online musicapplication). In this case, the social-networking system 160 may createa “listened” edge 406 and a “used” edge (as illustrated in FIG. 6)between user nodes 602 corresponding to the user and concept nodes 604corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 606 (asillustrated in FIG. 6) between concept nodes 604 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 606corresponds to an action performed by an external application on anexternal audio file (the song “Imagine”). Although this disclosuredescribes particular edges 606 with particular attributes connectinguser nodes 602 and concept nodes 604, this disclosure contemplates anysuitable edges 606 with any suitable attributes connecting user nodes602 and concept nodes 604. Moreover, although this disclosure describesedges between a user node 602 and a concept node 604 representing asingle relationship, this disclosure contemplates edges between a usernode 602 and a concept node 604 representing one or more relationships.As an example and not by way of limitation, an edge 606 may representboth that a user likes and has used at a particular concept.Alternatively, another edge 606 may represent each type of relationship(or multiples of a single relationship) between a user node 602 and aconcept node 604 (as illustrated in FIG. 6 between user node 602 foruser “E” and concept node 604).

In particular embodiments, the social-networking system 160 may createan edge 406 between a user node 602 and a concept node 604 in the socialgraph 600. As an example and not by way of limitation, a user viewing aconcept-profile interface (such as, for example, by using a web browseror a special-purpose application hosted by the user's client system 130)may indicate that he or she likes the concept represented by the conceptnode 604 by clicking or selecting a “Like” icon, which may cause theuser's client system 130 to send to the social-networking system 160 amessage indicating the user's liking of the concept associated with theconcept-profile interface. In response to the message, thesocial-networking system 160 may create an edge 606 between user node602 associated with the user and concept node 604, as illustrated by“like” edge 606 between the user and concept node 604. In particularembodiments, the social-networking system 160 may store an edge 606 inone or more data stores. In particular embodiments, an edge 606 may beautomatically formed by the social-networking system 160 in response toa particular user action. As an example and not by way of limitation, ifa first user uploads a picture, watches a movie, or listens to a song,an edge 606 may be formed between user node 602 corresponding to thefirst user and concept nodes 604 corresponding to those concepts.Although this disclosure describes forming particular edges 606 inparticular manners, this disclosure contemplates forming any suitableedges 606 in any suitable manner.

Vector Spaces and Embeddings

FIG. 7 illustrates an example view of a vector space 700. In particularembodiments, an object or an n-gram may be represented in ad-dimensional vector space, where d denotes any suitable number ofdimensions. Although the vector space 700 is illustrated as athree-dimensional space, this is for illustrative purposes only, as thevector space 700 may be of any suitable dimension. In particularembodiments, an n-gram may be represented in the vector space 700 as avector referred to as a term embedding. Each vector may comprisecoordinates corresponding to a particular point in the vector space 700(i.e., the terminal point of the vector). As an example and not by wayof limitation, vectors 710, 720, and 750 may be represented as points inthe vector space 700, as illustrated in FIG. 7. An n-gram may be mappedto a respective vector representation. As an example and not by way oflimitation, n-grams t₁ and t₂ may be mapped to vectors v₁ ^(→) and v₂^(→) in the vector space 700, respectively, by applying a function π^(→)defined by a dictionary, such that v₁ ^(→)=π^(→)(t₁) and v₂^(→)=π^(→)(t₂). As another example and not by way of limitation, adictionary trained to map text to a vector representation may beutilized, or such a dictionary may be itself generated via training. Asanother example and not by way of limitation, a model, such as Word2vec,may be used to map an n-gram to a vector representation in the vectorspace 700. In particular embodiments, an n-gram may be mapped to avector representation in the vector space 700 by using a machine leaningmodel (e.g., a neural network). The machine learning model may have beentrained using a sequence of training data (e.g., a corpus of objectseach comprising n-grams).

In particular embodiments, an object may be represented in the vectorspace 700 as a vector referred to as a feature vector or an objectembedding. As an example and not by way of limitation, objects e₁ and e₂may be mapped to vectors v₁ ^(→) and v₂ ^(→) in the vector space 700,respectively, by applying a function π^(→), such that v₁ ^(→)=π^(→)(e₁)and v₂ ^(→)=π^(→)(e₂) In particular embodiments, an object may be mappedto a vector based on one or more properties, attributes, or features ofthe object, relationships of the object with other objects, or any othersuitable information associated with the object. As an example and notby way of limitation, a function π^(→) may map objects to vectors byfeature extraction, which may start from an initial set of measured dataand build derived values (e.g., features). As an example and not by wayof limitation, an object comprising a video or an image may be mapped toa vector by using an algorithm to detect or isolate various desiredportions or shapes of the object. Features used to calculate the vectormay be based on information obtained from edge detection, cornerdetection, blob detection, ridge detection, scale-invariant featuretransformation, edge direction, changing intensity, autocorrelation,motion detection, optical flow, thresholding, blob extraction, templatematching, Hough transformation (e.g., lines, circles, ellipses,arbitrary shapes), or any other suitable information. As another exampleand not by way of limitation, an object comprising audio data may bemapped to a vector based on features such as a spectral slope, atonality coefficient, an audio spectrum centroid, an audio spectrumenvelope, a Mel-frequency cepstrum, or any other suitable information.In particular embodiments, when an object has data that is either toolarge to be efficiently processed or comprises redundant data, afunction π^(→) may map the object to a vector using a transformedreduced set of features (e.g., feature selection). In particularembodiments, a function π^(→) may map an object e to a vector π^(→)(e)based on one or more n-grams associated with object e. Although thisdisclosure describes representing an n-gram or an object in a vectorspace in a particular manner, this disclosure contemplates representingan n-gram or an object in a vector space in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a similarity metric of vectors in vector space 700. Asimilarity metric may be a cosine similarity, a Minkowski distance, aMahalanobis distance, a Jaccard similarity coefficient, or any suitablesimilarity metric. As an example and not by way of limitation, asimilarity metric of v₁ ^(→) and v₂ ^(→) may be a cosine similarity

$\frac{\overset{\rightharpoonup}{v_{1}} \cdot \overset{\rightharpoonup}{v_{2}}}{{\overset{\rightharpoonup}{v_{1}}}{\overset{\rightharpoonup}{v_{2}}}}.$As another example and not by way of limitation, a similarity metric ofv₁ ^(→) and v₂ ^(→) may be a Euclidean distance ∥v₁ ^(→)−v₂ ^(→)∥. Asimilarity metric of two vectors may represent how similar the twoobjects or n-grams corresponding to the two vectors, respectively, areto one another, as measured by the distance between the two vectors inthe vector space 700. As an example and not by way of limitation, vector710 and vector 720 may correspond to objects that are more similar toone another than the objects corresponding to vector 710 and vector 750,based on the distance between the respective vectors. Although thisdisclosure describes calculating a similarity metric between vectors ina particular manner, this disclosure contemplates calculating asimilarity metric between vectors in any suitable manner.

More information on vector spaces, embeddings, feature vectors, andsimilarity metrics may be found in U.S. patent application Ser. No.14/949,436, filed 23 Nov. 2015, U.S. patent application Ser. No.15/286,315, filed 5 Oct. 2016, and U.S. patent application Ser. No.15/365,789, filed 30 Nov. 2016, each of which is incorporated byreference.

Artificial Neural Networks

FIG. 8 illustrates an example artificial neural network (“ANN”) 800. Inparticular embodiments, an ANN may refer to a computational modelcomprising one or more nodes. Example ANN 800 may comprise an inputlayer 810, hidden layers 820, 830, 860, and an output layer 850. Eachlayer of the ANN 800 may comprise one or more nodes, such as a node 805or a node 815. In particular embodiments, each node of an ANN may beconnected to another node of the ANN. As an example and not by way oflimitation, each node of the input layer 810 may be connected to one ofmore nodes of the hidden layer 820. In particular embodiments, one ormore nodes may be a bias node (e.g., a node in a layer that is notconnected to and does not receive input from any node in a previouslayer). In particular embodiments, each node in each layer may beconnected to one or more nodes of a previous or subsequent layer.Although FIG. 8 depicts a particular ANN with a particular number oflayers, a particular number of nodes, and particular connections betweennodes, this disclosure contemplates any suitable ANN with any suitablenumber of layers, any suitable number of nodes, and any suitableconnections between nodes. As an example and not by way of limitation,although FIG. 8 depicts a connection between each node of the inputlayer 810 and each node of the hidden layer 820, one or more nodes ofthe input layer 810 may not be connected to one or more nodes of thehidden layer 820.

In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANNwith no cycles or loops where communication between nodes flows in onedirection beginning with the input layer and proceeding to successivelayers). As an example and not by way of limitation, the input to eachnode of the hidden layer 820 may comprise the output of one or morenodes of the input layer 810. As another example and not by way oflimitation, the input to each node of the output layer 850 may comprisethe output of one or more nodes of the hidden layer 860. In particularembodiments, an ANN may be a deep neural network (e.g., a neural networkcomprising at least two hidden layers). In particular embodiments, anANN may be a deep residual network. A deep residual network may be afeedforward ANN comprising hidden layers organized into residual blocks.The input into each residual block after the first residual block may bea function of the output of the previous residual block and the input ofthe previous residual block. As an example and not by way of limitation,the input into residual block N may be F(x)+x, where F(x) may be theoutput of residual block N−1, x may be the input into residual blockN−1. Although this disclosure describes a particular ANN, thisdisclosure contemplates any suitable ANN.

In particular embodiments, an activation function may correspond to eachnode of an ANN. An activation function of a node may define the outputof a node for a given input. In particular embodiments, an input to anode may comprise a set of inputs. As an example and not by way oflimitation, an activation function may be an identity function, a binarystep function, a logistic function, or any other suitable function. Asanother example and not by way of limitation, an activation function fora node k may be the sigmoid function

${{F_{k}\left( s_{k} \right)} = \frac{1}{1 + e^{- s_{k}}}},$the hyperbolic tangent function

${{F_{k}\left( s_{k} \right)} = \frac{e^{s_{k}} - e^{- s_{k}}}{e^{s_{k}} + e^{- s_{k}}}},$the rectifier F_(k)(s_(k))=max(0,s_(k)), or any other suitable functionF_(k)(s_(k)), where s_(k) may be the effective input to node k. Inparticular embodiments, the input of an activation functioncorresponding to a node may be weighted. Each node may generate outputusing a corresponding activation function based on weighted inputs. Inparticular embodiments, each connection between nodes may be associatedwith a weight. As an example and not by way of limitation, a connection825 between the node 805 and the node 815 may have a weightingcoefficient of 0.4, which may indicate that 0.4 multiplied by the outputof the node 805 is used as an input to the node 815. As another exampleand not by way of limitation, the output y_(k) of node k may bey_(k)=F_(k)(s_(k)), where F_(k) may be the activation functioncorresponding to node k, s_(k)=Σ_(j) (w_(jk)x_(j)) may be the effectiveinput to node k, x_(j) may be the output of a node j connected to nodek, and w_(jk) may be the weighting coefficient between node j and nodek. In particular embodiments, the input to nodes of the input layer maybe based on a vector representing an object. Although this disclosuredescribes particular inputs to and outputs of nodes, this disclosurecontemplates any suitable inputs to and outputs of nodes. Moreover,although this disclosure may describe particular connections and weightsbetween nodes, this disclosure contemplates any suitable connections andweights between nodes.

In particular embodiments, an ANN may be trained using training data. Asan example and not by way of limitation, training data may compriseinputs to the ANN800 and an expected output. As another example and notby way of limitation, training data may comprise vectors eachrepresenting a training object and an expected label for each trainingobject. In particular embodiments, training an ANN may comprisemodifying the weights associated with the connections between nodes ofthe ANN by optimizing an objective function. As an example and not byway of limitation, a training method may be used (e.g., the conjugategradient method, the gradient descent method, the stochastic gradientdescent) to backpropagate the sum-of-squares error measured as adistances between each vector representing a training object (e.g.,using a cost function that minimizes the sum-of-squares error). Inparticular embodiments, an ANN may be trained using a dropout technique.As an example and not by way of limitation, one or more nodes may betemporarily omitted (e.g., receive no input and generate no output)while training. For each training object, one or more nodes of the ANNmay have some probability of being omitted. The nodes that are omittedfor a particular training object may be different than the nodes omittedfor other training objects (e.g., the nodes may be temporarily omittedon an object-by-object basis). Although this disclosure describestraining an ANN in a particular manner, this disclosure contemplatestraining an ANN in any suitable manner.

Privacy

In particular embodiments, one or more objects (e.g., content or othertypes of objects) of a computing system may be associated with one ormore privacy settings. The one or more objects may be stored on orotherwise associated with any suitable computing system or application,such as, for example, a social-networking system 160, a client system130, an assistant system 140, a third-party system 170, asocial-networking application, an assistant application, a messagingapplication, a photo-sharing application, or any other suitablecomputing system or application. Although the examples discussed hereinare in the context of an online social network, these privacy settingsmay be applied to any other suitable computing system. Privacy settings(or “access settings”) for an object may be stored in any suitablemanner, such as, for example, in association with the object, in anindex on an authorization server, in another suitable manner, or anysuitable combination thereof. A privacy setting for an object mayspecify how the object (or particular information associated with theobject) can be accessed, stored, or otherwise used (e.g., viewed,shared, modified, copied, executed, surfaced, or identified) within theonline social network. When privacy settings for an object allow aparticular user or other entity to access that object, the object may bedescribed as being “visible” with respect to that user or other entity.As an example and not by way of limitation, a user of the online socialnetwork may specify privacy settings for a user-profile page thatidentify a set of users that may access work-experience information onthe user-profile page, thus excluding other users from accessing thatinformation.

In particular embodiments, privacy settings for an object may specify a“blocked list” of users or other entities that should not be allowed toaccess certain information associated with the object. In particularembodiments, the blocked list may include third-party entities. Theblocked list may specify one or more users or entities for which anobject is not visible. As an example and not by way of limitation, auser may specify a set of users who may not access photo albumsassociated with the user, thus excluding those users from accessing thephoto albums (while also possibly allowing certain users not within thespecified set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 404 corresponding to a particular photo may havea privacy setting specifying that the photo may be accessed only byusers tagged in the photo and friends of the users tagged in the photo.In particular embodiments, privacy settings may allow users to opt in toor opt out of having their content, information, or actionsstored/logged by the social-networking system 160 or assistant system140 or shared with other systems (e.g., a third-party system 170).Although this disclosure describes using particular privacy settings ina particular manner, this disclosure contemplates using any suitableprivacy settings in any suitable manner.

In particular embodiments, privacy settings may be based on one or morenodes or edges of a social graph 400. A privacy setting may be specifiedfor one or more edges 606 or edge-types of the social graph 600, or withrespect to one or more nodes 602, 604 or node-types of the social graph600. The privacy settings applied to a particular edge 606 connectingtwo nodes may control whether the relationship between the two entitiescorresponding to the nodes is visible to other users of the onlinesocial network. Similarly, the privacy settings applied to a particularnode may control whether the user or concept corresponding to the nodeis visible to other users of the online social network. As an exampleand not by way of limitation, a first user may share an object to thesocial-networking system 160. The object may be associated with aconcept node 604 connected to a user node 602 of the first user by anedge 606. The first user may specify privacy settings that apply to aparticular edge 606 connecting to the concept node 604 of the object, ormay specify privacy settings that apply to all edges 606 connecting tothe concept node 604. As another example and not by way of limitation,the first user may share a set of objects of a particular object-type(e.g., a set of images). The first user may specify privacy settingswith respect to all objects associated with the first user of thatparticular object-type as having a particular privacy setting (e.g.,specifying that all images posted by the first user are visible only tofriends of the first user and/or users tagged in the images).

In particular embodiments, the social-networking system 160 may presenta “privacy wizard” (e.g., within a webpage, a module, one or more dialogboxes, or any other suitable interface) to the first user to assist thefirst user in specifying one or more privacy settings. The privacywizard may display instructions, suitable privacy-related information,current privacy settings, one or more input fields for accepting one ormore inputs from the first user specifying a change or confirmation ofprivacy settings, or any suitable combination thereof. In particularembodiments, the social-networking system 160 may offer a “dashboard”functionality to the first user that may display, to the first user,current privacy settings of the first user. The dashboard functionalitymay be displayed to the first user at any appropriate time (e.g.,following an input from the first user summoning the dashboardfunctionality, following the occurrence of a particular event or triggeraction). The dashboard functionality may allow the first user to modifyone or more of the first user's current privacy settings at any time, inany suitable manner (e.g., redirecting the first user to the privacywizard).

Privacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, my boss), userswithin a particular degree-of-separation (e.g., friends,friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableentities, or any suitable combination thereof. Although this disclosuredescribes particular granularities of permitted access or denial ofaccess, this disclosure contemplates any suitable granularities ofpermitted access or denial of access.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and the object may be sentonly to the user (or a client system 130 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store 164 or may prevent the requested objectfrom being sent to the user. In the search-query context, an object maybe provided as a search result only if the querying user is authorizedto access the object, e.g., if the privacy settings for the object allowit to be surfaced to, discovered by, or otherwise visible to thequerying user. In particular embodiments, an object may representcontent that is visible to a user through a newsfeed of the user. As anexample and not by way of limitation, one or more objects may be visibleto a user's “Trending” page. In particular embodiments, an object maycorrespond to a particular user. The object may be content associatedwith the particular user, or may be the particular user's account orinformation stored on the social-networking system 160, or othercomputing system. As an example and not by way of limitation, a firstuser may view one or more second users of an online social networkthrough a “People You May Know” function of the online social network,or by viewing a list of friends of the first user. As an example and notby way of limitation, a first user may specify that they do not wish tosee objects associated with a particular second user in their newsfeedor friends list. If the privacy settings for the object do not allow itto be surfaced to, discovered by, or visible to the user, the object maybe excluded from the search results. Although this disclosure describesenforcing privacy settings in a particular manner, this disclosurecontemplates enforcing privacy settings in any suitable manner.

In particular embodiments, different objects of the same type associatedwith a user may have different privacy settings. Different types ofobjects associated with a user may have different types of privacysettings. As an example and not by way of limitation, a first user mayspecify that the first user's status updates are public, but any imagesshared by the first user are visible only to the first user's friends onthe online social network. As another example and not by way oflimitation, a user may specify different privacy settings for differenttypes of entities, such as individual users, friends-of-friends,followers, user groups, or corporate entities. As another example andnot by way of limitation, a first user may specify a group of users thatmay view videos posted by the first user, while keeping the videos frombeing visible to the first user's employer. In particular embodiments,different privacy settings may be provided for different user groups oruser demographics. As an example and not by way of limitation, a firstuser may specify that other users who attend the same university as thefirst user may view the first user's pictures, but that other users whoare family members of the first user may not view those same pictures.

In particular embodiments, the social-networking system 160 may provideone or more default privacy settings for each object of a particularobject-type. A privacy setting for an object that is set to a defaultmay be changed by a user associated with that object. As an example andnot by way of limitation, all images posted by a first user may have adefault privacy setting of being visible only to friends of the firstuser and, for a particular image, the first user may change the privacysetting for the image to be visible to friends and friends-of-friends.

In particular embodiments, privacy settings may allow a first user tospecify (e.g., by opting out, by not opting in) whether thesocial-networking system 160 or assistant system 140 may receive,collect, log, or store particular objects or information associated withthe user for any purpose. In particular embodiments, privacy settingsmay allow the first user to specify whether particular applications orprocesses may access, store, or use particular objects or informationassociated with the user. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed, stored,or used by specific applications or processes. The social-networkingsystem 160 or assistant system 140 may access such information in orderto provide a particular function or service to the first user, withoutthe social-networking system 160 or assistant system 140 having accessto that information for any other purposes. Before accessing, storing,or using such objects or information, the social-networking system 160or assistant system 140 may prompt the user to provide privacy settingsspecifying which applications or processes, if any, may access, store,or use the object or information prior to allowing any such action. Asan example and not by way of limitation, a first user may transmit amessage to a second user via an application related to the online socialnetwork (e.g., a messaging app), and may specify privacy settings thatsuch messages should not be stored by the social-networking system 160or assistant system 140.

In particular embodiments, a user may specify whether particular typesof objects or information associated with the first user may beaccessed, stored, or used by the social-networking system 160 orassistant system 140. As an example and not by way of limitation, thefirst user may specify that images sent by the first user through thesocial-networking system 160 or assistant system 140 may not be storedby the social-networking system 160 or assistant system 140. As anotherexample and not by way of limitation, a first user may specify thatmessages sent from the first user to a particular second user may not bestored by the social-networking system 160 or assistant system 140. Asyet another example and not by way of limitation, a first user mayspecify that all objects sent via a particular application may be savedby the social-networking system 160 or assistant system 140.

In particular embodiments, privacy settings may allow a first user tospecify whether particular objects or information associated with thefirst user may be accessed from particular client systems 130 orthird-party systems 170. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed from aparticular device (e.g., the phone book on a user's smart phone), from aparticular application (e.g., a messaging app), or from a particularsystem (e.g., an email server). The social-networking system 160 orassistant system 140 may provide default privacy settings with respectto each device, system, or application, and/or the first user may beprompted to specify a particular privacy setting for each context. As anexample and not by way of limitation, the first user may utilize alocation-services feature of the social-networking system 160 orassistant system 140 to provide recommendations for restaurants or otherplaces in proximity to the user. The first user's default privacysettings may specify that the social-networking system 160 or assistantsystem 140 may use location information provided from a client device130 of the first user to provide the location-based services, but thatthe social-networking system 160 or assistant system 140 may not storethe location information of the first user or provide it to anythird-party system 170. The first user may then update the privacysettings to allow location information to be used by a third-partyimage-sharing application in order to geo-tag photos.

Privacy Settings Based on Location

In particular embodiments, privacy settings may allow a user to specifyone or more geographic locations from which objects can be accessed.Access or denial of access to the objects may depend on the geographiclocation of a user who is attempting to access the objects. As anexample and not by way of limitation, a user may share an object andspecify that only users in the same city may access or view the object.As another example and not by way of limitation, a first user may sharean object and specify that the object is visible to second users onlywhile the first user is in a particular location. If the first userleaves the particular location, the object may no longer be visible tothe second users. As another example and not by way of limitation, afirst user may specify that an object is visible only to second userswithin a threshold distance from the first user. If the first usersubsequently changes location, the original second users with access tothe object may lose access, while a new group of second users may gainaccess as they come within the threshold distance of the first user.

Privacy Settings for User Authentication and Experience PersonalizationInformation

In particular embodiments, the social-networking system 160 or assistantsystem 140 may have functionalities that may use, as inputs, personal orbiometric information of a user for user-authentication orexperience-personalization purposes. A user may opt to make use of thesefunctionalities to enhance their experience on the online socialnetwork. As an example and not by way of limitation, a user may providepersonal or biometric information to the social-networking system 160 orassistant system 140. The user's privacy settings may specify that suchinformation may be used only for particular processes, such asauthentication, and further specify that such information may not beshared with any third-party system 170 or used for other processes orapplications associated with the social-networking system 160 orassistant system 140. As another example and not by way of limitation,the social-networking system 160 may provide a functionality for a userto provide voice-print recordings to the online social network. As anexample and not by way of limitation, if a user wishes to utilize thisfunction of the online social network, the user may provide a voicerecording of his or her own voice to provide a status update on theonline social network. The recording of the voice-input may be comparedto a voice print of the user to determine what words were spoken by theuser. The user's privacy setting may specify that such voice recordingmay be used only for voice-input purposes (e.g., to authenticate theuser, to send voice messages, to improve voice recognition in order touse voice-operated features of the online social network), and furtherspecify that such voice recording may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160. As another example and not by way oflimitation, the social-networking system 160 may provide a functionalityfor a user to provide a reference image (e.g., a facial profile, aretinal scan) to the online social network. The online social networkmay compare the reference image against a later-received image input(e.g., to authenticate the user, to tag the user in photos). The user'sprivacy setting may specify that such voice recording may be used onlyfor a limited purpose (e.g., authentication, tagging the user inphotos), and further specify that such voice recording may not be sharedwith any third-party system 170 or used by other processes orapplications associated with the social-networking system 160.

Systems and Methods

FIG. 9 illustrates an example computer system 900. In particularembodiments, one or more computer systems 900 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 900 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 900 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 900.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems900. This disclosure contemplates computer system 900 taking anysuitable physical form. As example and not by way of limitation,computer system 900 may be an embedded computer system, a system-on-chip(SOC), a single-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, a tablet computer system, or acombination of two or more of these. Where appropriate, computer system900 may include one or more computer systems 900; be unitary ordistributed; span multiple locations; span multiple machines; spanmultiple data centers; or reside in a cloud, which may include one ormore cloud components in one or more networks. Where appropriate, one ormore computer systems 900 may perform without substantial spatial ortemporal limitation one or more steps of one or more methods describedor illustrated herein. As an example and not by way of limitation, oneor more computer systems 900 may perform in real time or in batch modeone or more steps of one or more methods described or illustratedherein. One or more computer systems 900 may perform at different timesor at different locations one or more steps of one or more methodsdescribed or illustrated herein, where appropriate.

In particular embodiments, computer system 900 includes a processor 902,memory 904, storage 906, an input/output (I/O) interface 908, acommunication interface 910, and a bus 912. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 902 includes hardware for executinginstructions, such as those making up a computer program. As an exampleand not by way of limitation, to execute instructions, processor 902 mayretrieve (or fetch) the instructions from an internal register, aninternal cache, memory 904, or storage 906; decode and execute them; andthen write one or more results to an internal register, an internalcache, memory 904, or storage 906. In particular embodiments, processor902 may include one or more internal caches for data, instructions, oraddresses. This disclosure contemplates processor 902 including anysuitable number of any suitable internal caches, where appropriate. Asan example and not by way of limitation, processor 902 may include oneor more instruction caches, one or more data caches, and one or moretranslation lookaside buffers (TLBs). Instructions in the instructioncaches may be copies of instructions in memory 904 or storage 906, andthe instruction caches may speed up retrieval of those instructions byprocessor 902. Data in the data caches may be copies of data in memory904 or storage 906 for instructions executing at processor 902 tooperate on; the results of previous instructions executed at processor902 for access by subsequent instructions executing at processor 902 orfor writing to memory 904 or storage 906; or other suitable data. Thedata caches may speed up read or write operations by processor 902. TheTLBs may speed up virtual-address translation for processor 902. Inparticular embodiments, processor 902 may include one or more internalregisters for data, instructions, or addresses. This disclosurecontemplates processor 902 including any suitable number of any suitableinternal registers, where appropriate. Where appropriate, processor 902may include one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 902. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 904 includes main memory for storinginstructions for processor 902 to execute or data for processor 902 tooperate on. As an example and not by way of limitation, computer system900 may load instructions from storage 906 or another source (such as,for example, another computer system 900) to memory 904. Processor 902may then load the instructions from memory 904 to an internal registeror internal cache. To execute the instructions, processor 902 mayretrieve the instructions from the internal register or internal cacheand decode them. During or after execution of the instructions,processor 902 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor902 may then write one or more of those results to memory 904. Inparticular embodiments, processor 902 executes only instructions in oneor more internal registers or internal caches or in memory 904 (asopposed to storage 906 or elsewhere) and operates only on data in one ormore internal registers or internal caches or in memory 904 (as opposedto storage 906 or elsewhere). One or more memory buses (which may eachinclude an address bus and a data bus) may couple processor 902 tomemory 904. Bus 912 may include one or more memory buses, as describedbelow. In particular embodiments, one or more memory management units(MMUs) reside between processor 902 and memory 904 and facilitateaccesses to memory 904 requested by processor 902. In particularembodiments, memory 904 includes random access memory (RAM). This RAMmay be volatile memory, where appropriate. Where appropriate, this RAMmay be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 904 may include one ormore memories 904, where appropriate. Although this disclosure describesand illustrates particular memory, this disclosure contemplates anysuitable memory.

In particular embodiments, storage 906 includes mass storage for data orinstructions. As an example and not by way of limitation, storage 906may include a hard disk drive (HDD), a floppy disk drive, flash memory,an optical disc, a magneto-optical disc, magnetic tape, or a UniversalSerial Bus (USB) drive or a combination of two or more of these. Storage906 may include removable or non-removable (or fixed) media, whereappropriate. Storage 906 may be internal or external to computer system900, where appropriate. In particular embodiments, storage 906 isnon-volatile, solid-state memory. In particular embodiments, storage 906includes read-only memory (ROM). Where appropriate, this ROM may bemask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM),electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM),or flash memory or a combination of two or more of these. Thisdisclosure contemplates mass storage 906 taking any suitable physicalform. Storage 906 may include one or more storage control unitsfacilitating communication between processor 902 and storage 906, whereappropriate. Where appropriate, storage 906 may include one or morestorages 906. Although this disclosure describes and illustratesparticular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 908 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 900 and one or more I/O devices. Computer system900 may include one or more of these I/O devices, where appropriate. Oneor more of these I/O devices may enable communication between a personand computer system 900. As an example and not by way of limitation, anI/O device may include a keyboard, keypad, microphone, monitor, mouse,printer, scanner, speaker, still camera, stylus, tablet, touch screen,trackball, video camera, another suitable I/O device or a combination oftwo or more of these. An I/O device may include one or more sensors.This disclosure contemplates any suitable I/O devices and any suitableI/O interfaces 908 for them. Where appropriate, I/O interface 908 mayinclude one or more device or software drivers enabling processor 902 todrive one or more of these I/O devices. I/O interface 908 may includeone or more I/O interfaces 908, where appropriate. Although thisdisclosure describes and illustrates a particular I/O interface, thisdisclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 910 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 900 and one or more other computer systems 900 or one ormore networks. As an example and not by way of limitation, communicationinterface 910 may include a network interface controller (NIC) ornetwork adapter for communicating with an Ethernet or other wire-basednetwork or a wireless NIC (WNIC) or wireless adapter for communicatingwith a wireless network, such as a WI-FI network. This disclosurecontemplates any suitable network and any suitable communicationinterface 910 for it. As an example and not by way of limitation,computer system 900 may communicate with an ad hoc network, a personalarea network (PAN), a local area network (LAN), a wide area network(WAN), a metropolitan area network (MAN), or one or more portions of theInternet or a combination of two or more of these. One or more portionsof one or more of these networks may be wired or wireless. As anexample, computer system 900 may communicate with a wireless PAN (WPAN)(such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAXnetwork, a cellular telephone network (such as, for example, a GlobalSystem for Mobile Communications (GSM) network), or other suitablewireless network or a combination of two or more of these. Computersystem 900 may include any suitable communication interface 910 for anyof these networks, where appropriate. Communication interface 910 mayinclude one or more communication interfaces 910, where appropriate.Although this disclosure describes and illustrates a particularcommunication interface, this disclosure contemplates any suitablecommunication interface.

In particular embodiments, bus 912 includes hardware, software, or bothcoupling components of computer system 900 to each other. As an exampleand not by way of limitation, bus 912 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 912may include one or more buses 912, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by one or more computingsystems: receiving, from a client system associated with a first user, afirst user input by the first user, wherein the first user input isassociated with a current dialog session; identifying a first languageregister associated with the first user based on the first user input;accessing a plurality of language-register models associated with aplurality of language registers stored in a data store, wherein theplurality of language-register models are each personalized with respectto the first user; selecting a first language-register model from theplurality of language-register models based on the identified firstlanguage register; and generating a first communication contentresponsive to the first user input, the first communication contentbeing personalized for the first user based on the selected firstlanguage-register model.
 2. The method of claim 1, further comprising:identifying an intent and one or more slots associated with the firstuser input; determining an action based on the identified intent;executing the action to retrieve information associated with the one ormore slots from one or more information sources; accessing a pluralityof language-generation templates stored in the data store, wherein eachlanguage-generation template comprises one or more slots correspondingto the one or more slots associated with the first user input; andselecting a language-generation template from the plurality oflanguage-generation templates based on the identified intent.
 3. Themethod of claim 2, wherein the one or more information sources compriseone or more of: one or more first-party agents; or one or morethird-party agents.
 4. The method of claim 2, wherein generating thecommunication content for the first user is further based on theretrieved information and the selected language-generation template. 5.The method of claim 4, wherein generating the communication content forthe first user comprises: generating a list of candidate words based onthe identified language register and the retrieved information; pruningthe list of candidate words; and inserting, from the pruned list ofcandidate words, one of the candidate words into each slot of theselected language-generation template.
 6. The method of claim 1, furthercomprising: receiving, from the client system, a second user input bythe first user, wherein the second user input is associated with thecurrent dialog session; identifying a second language registerassociated with the first user based on the second user input; accessingthe plurality of language-register models associated with the pluralityof language registers stored in the data store; selecting a secondlanguage-register model from the plurality of language-register modelsbased on the identified second language register; and generating asecond communication content responsive to the second user input, thesecond communication content being personalized for the first user basedon the selected second language-register model.
 7. The method of claim1, wherein the first language register is identified based on amachine-learning model.
 8. The method of claim 1, wherein the first useris in the current dialog session with a second user, and wherein thecurrent dialog session comprises a message thread between the first userand the second user.
 9. The method of claim 8, further comprising:sending, to the client system, instructions for presenting the generatedfirst communication content to the first user, wherein the generatedfirst communication content is operable to allow the first user toselect the generated first communication content; receiving, from theclient system, a selection of the generated first communication contentfrom the first user; and inserting the generated first communicationcontent in the message thread between the first user and the seconduser.
 10. The method of claim 1, wherein the first user is in thecurrent dialog session with an assistant xbot, and wherein the currentdialog session comprises a message thread between the first user and theassistant xbot.
 11. The method of claim 10, further comprising:inserting the generated first communication content in the messagethread between the first user and the assistant xbot.
 12. The method ofclaim 1, wherein the first user input comprises one or more of: acharacter string; an audio clip; an image; or a video.
 13. The method ofclaim 1, wherein the generated first communication content comprises oneor more of: a character string; an audio clip; an image; or a video. 14.The method of claim 1, wherein the plurality of language-register modelsare trained based on a word-embedding model.
 15. The method of claim 14,wherein the word-embedding model is based on convolutional neuralnetwork.
 16. The method of claim 1, wherein each language-register modelis trained based on a plurality of training samples associated with thefirst user, the training samples comprising one or more of news feedposts, news feed comments, a user profile, or messages.
 17. The methodof claim 16, further comprising clustering the plurality of trainingsamples into one or more groups of training samples based on one or morecriteria.
 18. The method of claim 17, wherein the one or more criteriacomprise one or more of age, relationship, location, education,interest, or native language.
 19. The method of claim 17, furthercomprising training, for each group, a language-register model for thegroup based on the training samples associated with the group.
 20. Themethod of claim 1, wherein identifying the first language registercomprises: generating a first feature vector representing the first userinput; accessing a plurality of second feature vectors representing theplurality of language registers stored in the data store; calculating aplurality of similarity scores between the first feature vector and therespective second feature vector; and identifying the first languageregister based on the plurality of similarity scores.
 21. One or morecomputer-readable non-transitory storage media embodying software thatis operable when executed to: receive, from a client system associatedwith a first user, a first user input by the first user, wherein thefirst user input is associated with a current dialog session; identify afirst language register associated with the first user based on thefirst user input; access a plurality of language-register modelsassociated with a plurality of language registers stored in a datastore, wherein the plurality of language-register models are eachpersonalized with respect to the first user; select a firstlanguage-register model from the plurality of language-register modelsbased on the identified first language register; and generate a firstcommunication content responsive to the first user input, the firstcommunication content being personalized for the first user based on theselected first language-register model.
 22. A system comprising: one ormore processors; and a non-transitory memory coupled to the processorscomprising instructions executable by the processors, the processorsoperable when executing the instructions to: receive, from a clientsystem associated with a first user, a first user input by the firstuser, wherein the first user input is associated with a current dialogsession; identify a first language register associated with the firstuser based on the first user input; access a plurality oflanguage-register models associated with a plurality of languageregisters stored in a data store, wherein the plurality oflanguage-register models are each personalized with respect to the firstuser; select a first language-register model from the plurality oflanguage-register models based on the identified first languageregister; and generate a first communication content responsive to thefirst user input, the first communication content being personalized forthe first user based on the selected first language-register model.